Growth models for young stands : development and evaluation of growth models for commercial forests in Sweden

In long-term forest management planning systems, growth simulators are important. Through growth simulation the consequences of different silvicultural regimes can be evaluated, and suitable management schedules be chosen. This thesis deals with different aspects on growth of trees in young stands, i.e. stands between 0.5 and 8 metres height. The overall aim is to improve the accuracy of growth simulation in young stands in Sweden. The thesis comprises five papers, dealing with three main topics: (i) site productivity, (ii) evaluation of predictive models for basal area and height increment of single trees, and (iii) uncertainty in growth predictions. In the first study, 91 Norway spruce (Picea abies, (L.) Karst.) plantations in northern Sweden were investigated. Site index was estimated using current Swedish practices, and the expected yield was estimated based on the site index assessments. The study showed that there is a bias in the Swedish system for site index classification, and that this bias may lead to severe underestimates (about 35 %) of the yield capacity of Norway spruce stands in the area. In the second study, based on Scots pine (Pinus sylvestris, L.) plots, the stability of site index estimates between stands established at different points of times was analysed. The study did not reveal any major trend in estimated site indices in stands on similar sites, established at different points of time. However, there was a significant difference in site index between stands established before and after 1940. A conclusion was that these differences were due to differences in stand establishment and silvicultural practices. Two studies have been conducted, where new models for predicting basal area and height increment of single trees in young stands are presented. In some of these models, tree age was not included as a predictor variable. Although an accurately determined age of a young tree generally contributes to the accuracy of the growth forecast, the uncertainty in age determination makes the contribution of age as a predictor variable doubtful. Common for all models evaluated was that site productivity was modelled indirectly through the incorporation of temperature sum and variables describing site conditions, such as vegetation type and soil moisture. In one of the studies, the uncertainty of yield forecasts was modelled by specifically accounting for the random components of growth. Through Monte-Carlo simulation, distributions of yield estimates rather than point estimates were predicted. Single-tree growth models were derived using a mixed model regression approach. An example is given regarding how the estimated distributions can be used in a Bayesian approach to forest management planning, whereby the precision of the yield estimates will influence what decisions should optimally be carried out